Signal processor and radar apparatus

ABSTRACT

In order to accurately identify a target object, a signal processor is provided, which includes an extracting module configured to extract, from echo sample sequences, a plurality of samples caused by the target object as a partial sample sequence, a characteristic amount calculating module configured to calculate a characteristic of the partial sample sequence as a characteristic amount, a memory configured to store a plurality of type-based data that are data as comparison targets of the characteristic amount and correspond to types from which the target object is identified, and an identifying module configured to compare the characteristic amount with each of the plurality of type-based data and, based on the comparison result, identify the target object corresponding to the partial sample sequence for which the characteristic amount is calculated.

TECHNICAL FIELD

The present disclosure relates to a signal processor which detects atarget object from echo signals, and to a radar apparatus which includesthe signal processor.

BACKGROUND ART

As one example of conventional signal processors, Patent Document 1discloses a target identifying device which uses chronologicalinformation of amplitudes of echo signals as it is to identify an objectecho extracted from input signals. Particularly in this targetidentifying device, an identifying module receives a radar parameter anda distance-based partial waveform sequence from a reception waveformprocessing module, searches for and acquires a stored model parameterset, calculates a probability of outputting the distance-based partialwaveform sequence according to each model parameter, and presents, as anidentification result, a type of target which is included in a modelcalculation parameter corresponding to the model parameter with which ahighest output probability is obtained. Thus, a target wave and anunnecessary wave are identified based on waveform.

REFERENCE DOCUMENT OF CONVENTIONAL ART Patent Document

Patent Document 1: JP2005-214723A

DESCRIPTION OF THE DISCLOSURE Problem to be Solved by the Disclosure

However, in Patent Document 1, a probability model expressing atransition of an amplitude is required, and a problem of erroneouslyidentifying the target when a transition with a low output probabilityin a stored probability model is observed occurs.

The present disclosure is to solve the above problem and aims toaccurately identify a target object.

SUMMARY OF THE DISCLOSURE

(1) In order to solve the problem described above, according to oneaspect of the present disclosure, a signal processor for detecting atarget object by using a transducer for transmitting a transmission waveand receiving a reflection wave of the transmission wave, based on areception signal obtained from the reflection wave, may be provided. Thesignal processor may include an extracting module, a characteristicamount calculating module, a memory, and an identifying module. Theextracting module may extract, from echo sample sequences each of whichis obtained by plotting a plurality of samples constituting thereception signal on coordinates defined by a distance from thetransducer and amplitudes of the plurality of samples and is generatedfor each azimuth with reference to a position of the transducer, aplurality of samples caused by the target object as a partial samplesequence. The characteristic amount calculating module may calculate acharacteristic of the partial sample sequence as a characteristicamount. The memory may store a plurality of type-based data that aredata as comparison targets of the characteristic amount and correspondto types from which the target object is identified. The identifyingmodule may compare the characteristic amount with each of the pluralityof type-based data and, based on the comparison result, identify thetype of the target object for which the characteristic amount iscalculated.

(2) The characteristic amount calculating module may calculate theplurality of characteristic amounts and generate a characteristic vectorfrom the plurality of characteristic amounts.

(3) The characteristic amount may be one of the number of samplesconstituting a rising portion of the partial sample sequence, the numberof samples constituting a falling portion of the partial samplesequence, a highest value of the amplitudes of the samples included inthe partial sample sequence, and a value obtained based on an integralvalue of the falling portion.

(4) The identifying module may identify the target object based onsimilarity between the characteristic amount and the type-based data.

(5) The similarity may be calculated based on a difference between thecharacteristic amount and the type-based data.

(6) The type may correspond to the size of the target object.

(7) The memory may further store unnecessary object identification dataas data that is a comparison target of the characteristic amount. Theidentifying module may compare the characteristic amount with theunnecessary object identification data and identify, based on thecomparison result, that the partial sample sequence for which thecharacteristic amount is calculated is not caused by the target object.

(8) The signal processor may further include a deterioration degreeestimating module configured to estimate a deterioration degree that isa degree of deterioration of the partial sample sequence, the memorystoring the plurality of type-based data classified for eachdeterioration degree, as type-based data groups for each deteriorationdegree, and a data group selecting module configured to select, based onthe deterioration degree estimated by the deterioration degreeestimating module, a type-based data group for each deterioration degreeto be compared with the partial sample sequence of which thedeterioration degree is estimated. The identifying module may comparethe characteristic amount with each of the plurality of type-based dataconstituting the type-based data group for each deterioration degreeselected by the data group selecting module.

Echoes caused by other objects (rain, sea clutter, etc.) may besuperimposed on the echo caused by the target object, which may causedistortion of the echo of the target object desired to be observed. Thedeterioration degree herein may be an index indicating the degree ofdifference of a partial sample sequence actually extracted (a partialsample sequence superimposed by the echoes of the other objects such asrain), from the partial sample sequence corresponding to only the targetobject.

(9) The plurality of types from which the target object is identifiedmay include at least a large object and a small object indicating thesize of the target object. When a position of a certain one of thepartial sample sequences that is identified to be the small object and aposition of another one of the partial sample sequences that isidentified to be the small object have at least a given distance fromeach other, the identifying module may identify that the certain partialsample sequence is not caused by the target object.

(10) The plurality of types from which the target object is identifiedmay be determined in advance according to the size of the target object.When a close-side partial sample sequence and a far-side partial samplesequence that are at least two partial sample sequences are extractedfrom the echo sample sequences, and the size of the target objectindicated by the type of the far-side partial sample sequence that has alonger distance than the close-side partial sample sequence from thetransducer is smaller than the size of the target object indicated bythe type of the close-side partial sample sequence, the identifyingmodule may identify that the far-side partial sample sequence is notcaused by the target object.

(11) In order to solve the problem described above, according to oneaspect of the present disclosure, a radar apparatus may include atransducer configured to transmit a transmission wave and receive areflection wave of the transmission wave, any one of the describedsignal processors, and a display unit configured to display theidentification result of the signal processor.

(12) The radar apparatus, which may be equipped on a ship, may furtherinclude a positioning unit configured to perform positioning of theship, and a buoy information acquirer configured to store nautical chartinformation including positions of buoys and acquire positionalinformation of a buoy within a given distance range from the ship amongthe buoys in the nautical chart information. The identifying module mayidentify that the target object is the buoy based on the positionalinformation of the buoy acquired by the buoy information acquirer andpositional information of the target object for which the partial samplesequence extracted by the extracting module of the signal processor isgenerated.

Effect of the Disclosure

According to the present disclosure, the target object may accurately beidentified.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of a radar apparatus according to oneembodiment of the present disclosure.

FIG. 2 is a block diagram of an echo identification processorillustrated in FIG. 1.

FIG. 3 is a view schematically illustrating one example of a radar imagegenerated by a radar image generating module illustrated in FIG. 1.

FIG. 4 is a graph illustrating one example of an echo sample sequence.

FIG. 5 is an enlarged view around a peak waveform Pk1 of the echo samplesequence illustrated in FIG. 4, illustrating a generating method of acharacteristic vector.

FIG. 6 is a view illustrating a characteristic vector and acharacteristic vector graph generated from the characteristic vector.

FIG. 7 shows views illustrating templates stored in a memory.

FIG. 8 is a view illustrating one example of a display image generatedby a display image generating module illustrated in FIG. 1, which is animage displayed on a display unit.

FIG. 9 is a block diagram of an echo identification processor of a radarapparatus according to a modification.

FIG. 10 is a flowchart illustrating an operation of the echoidentification processor illustrated in FIG. 9.

FIG. 11 is a graph illustrating one example of an echo sample sequence.

FIG. 12 shows schematic views of templates stored in a memoryillustrated in FIG. 9.

FIG. 13 is a block diagram of an echo identification processor of aradar apparatus according to a modification.

FIG. 14 shows views illustrating templates stored in a memoryillustrated in FIG. 13.

FIG. 15 is a block diagram of an echo identification processor of aradar apparatus according to a modification.

FIG. 16 is a view illustrating one example of an image displayed on adisplay unit illustrated in FIG. 15.

FIG. 17 is a block diagram of an echo identification processor of aradar apparatus according to a modification.

FIG. 18 is a view illustrating one example of an image displayed on adisplay unit illustrated in FIG. 17.

FIG. 19 is a view illustrating one example of the image displayed on thedisplay unit illustrated in FIG. 17, illustrating a different displayexample from FIG. 18.

FIG. 20 is a block diagram of a radar apparatus according to amodification.

FIG. 21 is a block diagram of an echo identification processorillustrated in FIG. 20.

MODE FOR CARRYING OUT THE DISCLOSURE

Hereinafter, one embodiment of a radar apparatus 1 including an echoidentification processor 10 as a signal processor according to thepresent disclosure is described with reference to the accompanyingdrawings. The present disclosure is broadly applicable as a signalprocessor which detects a target object from an echo signal, and a radarapparatus including the signal processor.

FIG. 1 is a block diagram of the radar apparatus 1 according to thisembodiment of the present disclosure. Further, FIG. 2 is a block diagramof the echo identification processor 10 illustrated in FIG. 1. The radarapparatus 1 of this embodiment may be, for example, a ship radar andmainly used to detect objects, such as other ships. Further, the radarapparatus 1 may be configured to be capable of tracking an objectselected as a tracked object. Note that in the following description, aship equipped with the radar apparatus 1 may be referred to as “theship.”

As illustrated in FIG. 1, the radar apparatus 1 may include an antennaunit 2, a radar image generating module 3, a tracking processing module4, the echo identification processor 10, a display image generatingmodule 5, and a display unit 6.

The antenna unit 2 may include an antenna 2 a, a receiver 2 b, and anA/D converter 2 c.

The antenna 2 a may be a radar antenna capable of transmitting a pulsedradio wave (transmission wave) having high directivity. The antenna 2 amay also be configured to receive a reflection wave from an object. Theradar apparatus 1 may measure a time length from a transmission of thepulsed radio wave to a reception of the reflection wave. As a result,the radar apparatus 1 may detect a distance to the object. The antenna 2a may be configured to be capable of rotating 360° on a horizontalplane. The antenna 2 a may be configured to repeatedly transmit andreceive the radio wave at every given timing while changing thetransmission direction of the pulsed radio wave (while changing theantenna angle). With the above configuration, the radar apparatus 1 maydetect the object on a plane around the ship over 360°.

Note that, in the following description, an operation starting from atransmission of the pulse-shaped radio wave to the next transmission ofthe pulse-shaped radio wave may be referred to as “one sweep.” Moreover,an operation of rotating the antenna 360° while performing thetransmission and reception of the radio wave may be referred to as “onescan.”

The receiver 2 b may detect and amplify an echo signal obtained from thereflection wave received by the antenna 2 a. The receiver 2 b may outputthe amplified echo signal to the A/D converter 2 c. The A/D converter 2c may sample the echo signal in analog and convert it into digital data(echo data) composed of a plurality of bits. Here, the echo data mayinclude data specifying the intensity (signal level) of the echo signalobtained from the reflection wave received by the antenna 2 a. The A/Dconverter 2 c may output the echo data to the radar image generatingmodule 3, the tracking processing module 4, and the echo identificationprocessor 10.

FIG. 3 is a view schematically illustrating one example of a radar imagePr generated by the radar image generating module 3. Based on the echodata outputted from the A/D converter 2 c, the radar image generatingmodule 3 may generate the radar image Pr over 360° in the horizontalplane around a ship position S. The radar image Pr generated by theradar image generating module 3 may be outputted to the display imagegenerating module 5. In the example illustrated in FIG. 3, five echoimages TGn of target objects (“n” is a natural number and assignedcorresponding to each target object) may be displayed on a displayscreen. Note that, the density of dots in FIG. 3 may correspond to theecho intensity of the reflection wave from the object. For example, anarea where echoes with high intensity are observed may be illustratedwith high density dots, and an area where echoes with low intensity areobserved may be illustrated with low density dots. Echoes displayed in acenter section of the display screen may be reflection waves from a seasurface (a sea clutter) near the ship position S.

The tracking processing module 4 may be configured to specify a trackedobject (target object) based on the echo data outputted from the A/Dconverter 2 c and perform tracking processing in which the trackedobject is tracked. For example, the tracking processing module 4 maycalculate coordinates and estimated velocity vector of the trackedobject based on a velocity vector estimated from coordinates of thetracked object in the previous scanning. The tracking processing module4 may output the calculated coordinates and estimated velocity vector ofthe tracked object to the display image generating module 5. Note that,detailed explanation of the processing performed by the trackingprocessing module 4 is omitted since it may be similar to the processingperformed by a conventionally known tracking processor.

The echo identification processor 10 may extract the target object basedon the echo data outputted from the receiver 2 b, identify which type ofobject the target object is (e.g., a large ship, a medium ship, or asmall ship), and output the identification result to the display imagegenerating module 5. As illustrated in FIG. 2, the echo identificationprocessor 10 may include an extracting module 11, a characteristicvector generating module 12, a memory 13, and an identifying module 14.

FIG. 4 is a graph illustrating one example of an echo sample sequenceES. Further, FIG. 5 is an enlarged view around a peak waveform Pk1 ofthe echo sample sequence ES illustrated in FIG. 4. The echo samplesequence ES may be a graph obtained by plotting on coordinates aplurality of samples which are obtained from a single sweep. In thecoordinates, as illustrated in FIGS. 4 and 5, the horizontal axis is asample number (corresponding to the distance from the ship position S)and the vertical axis indicates the amplitude of the echo. Theextracting module 11 may generate the echo sample sequence ES for everysweep. Note that, the echo sample sequence ES illustrated in FIG. 4 maybe generated based on the reflection waves of the transmission wavestransmitted in the direction of a straight line L in FIG. 3.

With reference to FIG. 5, the extracting module 11 may extract a portionof the echo sample sequence ES which is caused by the target object, asa partial sample sequence Rn (Smp1, . . . , Smp2, . . . , Smp3). Forexample, with reference to FIG. 4, the extracting module 11 may compareeach of the sample data constituting the echo sample sequence with athreshold Thr stored in the extracting module 11 itself, and if at leasta given number of successive sample data exceed the threshold Thr in adistance direction, the extracting module 11 may extract these sampledata (Smp1, . . . , Smp2, . . . , Smp3 in the example illustrated inFIG. 5) as the partial sample sequence Rn. The partial sample sequenceRn thus extracted may be outputted to the characteristic vectorgenerating module 12. Note that, in the example illustrated in FIG. 4,two partial sample sequences R1 and R2 corresponding to the two peakwaveforms Pk1 and Pk2 are extracted, respectively.

With reference to FIG. 5, the characteristic vector generating module 12may calculate a plurality of characteristic amounts for each partialsample sequence Rn, and generate a characteristic vector from theplurality of calculated characteristic amounts. In order to generate thecharacteristic vector, the characteristic vector generating module 12may first calculate first to fourth characteristic amounts C1 to C4.

With reference to FIG. 5, the first characteristic amount C1 may becalculated based on the number N1 of samples in a rising portion of thepeak waveform Pk1 (i.e., the number of successive samples from thesample Smp1 which exceeds the threshold Thr to the sample Smp2 which hasa peak value Vp). In this embodiment, the first characteristic amount C1may be calculated as a normalized value so that the number N1 of thesamples falls within a range of 0 to 5.

The second characteristic amount C2 may be calculated based on the peakvalue Vp of the peak waveform Pk1. In this embodiment, the secondcharacteristic amount C2 may be calculated as a normalized value so thatthe peak value Vp falls within the range of 0 to 5.

The third characteristic amount C3 may be calculated based on the numberN2 of samples in a falling portion of the peak waveform Pk1 (i.e., thenumber of successive samples from the sample Smp2 which has the peakvalue Vp to the sample Smp3 which falls below the threshold Thr). Inthis embodiment, the third characteristic amount C3 may be calculated asa normalized value so that the number of samples falls within the rangeof 0 to 5.

The fourth characteristic amount C4 may be calculated based on a valueobtained through dividing a value A1 by a value A2 (A1/A2). The value A1may be obtained by integrating values obtained through reducing the peakvalue Vp by the respective amplitudes of the successive samples from thesample Smp2 which has the peak value Vp to the sample Smp3 which fallsbelow the threshold Thr. The value A2 may be obtained throughintegrating the amplitudes of the successive samples from the sampleSmp2 to the sample Smp3. That is, the fourth characteristic amount maybe calculated based on the value (A1/A2) obtained through dividing thearea of the hatched portion in FIG. 5 by the area of the cross-hatchedportion. In this embodiment, the fourth characteristic amount C4 may becalculated as a normalized value so that the value of A1/A2 falls withinthe range of 0 to 5.

FIG. 6 is a view illustrating a characteristic vector CV and acharacteristic vector graph G_(CV) generated from the characteristicvector CV. The characteristic vector generating module 12 may generatethe characteristic vector CV by grouping the first to fourthcharacteristic amounts C1 to C4 calculated corresponding to each partialsample sequence Rn as one set. Then, the characteristic vectorgenerating module 12 may generate the characteristic vector graph G_(CV)as illustrated in FIG. 6 from the characteristic vector CV. Thecharacteristic vector graph G_(CV) may be a graph generated by plottingthe characteristic amounts C1 to C4 on the axes of orthogonalcoordinates and connecting the characteristic amounts C1 to C4 adjacentto each other in the circumferential direction with straight lines.

FIG. 7 shows schematic views of templates TP (type-based data) stored inthe memory 13. Each template TP may be a graph having the samecoordinates as the characteristic vector graph G_(CV) and prepared inadvance by experiments etc. For example, the template TP may be acharacteristic vector graph generated from the characteristic vectorobtained from the reflection waves by conducting an experiment on anobject of which shape was already known. The templates TP may include,for example, large ship templates TP_(L), medium ship templates TP_(M),and small ship templates TPs which are for identifying the target objectby the identifying module 14 described later in detail. Each template TPmay be generated as an identification characteristic vector graph whichis compared with the characteristic vector graph G_(CV) to identify thesize of the ship corresponding to the target object having thecharacteristic vector. Note that, the characteristic amounts of eachtemplate TP illustrated in FIG. 7 may merely be illustrated as anexample and therefore irrelevant to the characteristic amounts obtainedby actual experiments etc.

The characteristic vector of the ship may vary depending on the size ofthe ship. Particularly, for example, a highest value of a partial samplesequence obtained from echoes of a large ship (that is, thecharacteristic amount C2) may become higher than a highest value of apartial sample sequence obtained from echoes of a small ship. Thus, thesize of the target object (ship) may be estimated by preparing, inadvance for every size of the ship, the template TP of thecharacteristic vector which may vary depending on the size of the ship,and comparing the characteristic vector CV of the target object witheach template TP stored in the memory 13.

Further, the characteristic vector of the ship may also vary dependingon the orientation of a ship (other ship) with respect to the ship, inother words, the orientation of the other ship when seen from the ship.In this regard, the memory 13 may store a plurality of templates TP ofthe characteristic vectors different from each other depending on theorientation of the ship, for each of the large ship, the medium ship,and the small ship.

The identifying module 14 may compare the characteristic vector graphG_(CV) generated by the characteristic vector generating module 12 withall the templates TP stored in the memory 13 and, based on thecomparison result, identify the size of the ship which is the targetobject (one of the large, medium and small ships). To be slightlyspecific, the identifying module 14 may calculate similarity between thecharacteristic vector graph G_(CV) and each template TP, and determinethe size of the ship indicated by the template TP having the highestsimilarity as the size of the ship. The identifying module 14 mayidentify the sizes of the target objects (ships) corresponding to allthe partial sample sequences Rn extracted by the extracting module 11.

For example, the identifying module 14 may calculate, as the similarity,a Euclidean distance between a four-dimensional spatial position definedby the characteristic amounts C1 to C4 constituting the characteristicvector graph G_(CV) of the target object and a four-dimensional spatialposition defined by characteristic amounts C1 tmp to C4 tmp constitutingeach template. The identifying module 14 may further determine the sizeindicated by the template which has the smallest Euclidean distance, asthe size of the target object. The identification result of theidentifying module 14 (whether the target object is the large ship, themedium ship, or the small ship) may be notified to the display imagegenerating module 5.

FIG. 8 is a view illustrating one example of a display image P generatedby the display image generating module 5, which is an image displayed onthe display unit 6. The display image generating module 5 may generatethe display image P displayed on the display unit 6 based on the radarimage generated by the radar image generating module 3, the coordinatesand estimated velocity vector of the tracked object notified from thetracking processing module 4, and the identification result of thetarget object obtained by the echo identification processor 10.

For example, based on the coordinates of the tracked object notifiedfrom the tracking processing module 4, the display image generatingmodule 5 may generate an image of a marker MKn surrounding the echoimage TGn to indicate that the echo image TGn is a tracked object. Thesize of this marker MKn may be determined based on the identificationresult of the target object (tracked object) identified by theidentifying module 14. In the display image P illustrated in FIG. 8, anecho image TG1 identified as a large ship may be surrounded by acircular marker MK1 having a relatively large radius, an echo image TG3identified as a medium ship may be surrounded by a circle marker MK3having a smaller radius than the marker MK1, and echo images TG2, TG4and TG5 identified as small ships may be surrounded by circular markersMK2, MK4 and MK5 having a smaller radius than the marker MK3. Thus, theuser may grasp the size of each ship.

EFFECTS

As described above, in the radar apparatus 1 according to thisembodiment, the size of the target object may be known based on the sizeof the marker MKn displayed superimposed on the echo image TGn of eachtarget object.

Further, as described above, the echo identification processor 10 mayidentify the target object based on the comparison result of thecharacteristic amounts C1 to C4 generated from the partial samplesequence Rn corresponding to each target object with the templates TP.In this manner, the risk of erroneously identifying the target objectmay be reduced compared with using the chronological information of theamplitudes of the echo signals as it is as in the conventional case.

To describe this point more in detail, in the conventional case, sincethe chronological information of the amplitude of the echo signal isused as it is, even when the echo amplitude changes instantaneously(that is, in a relatively short time) due to noises etc., this changemay influence the identification of the target object, causing erroneousidentification of the target object.

On the other hand, in the radar apparatus of this embodiment, thecharacteristics of the partial sample sequence Rn obtained for eachtarget object may be calculated as the characteristic amounts C1 to C4,and the target object may be identified based on the characteristicamounts C1 to C4. Thus, it may be prevented that the instantaneouschange of the echo amplitude greatly influences the identificationresult of the target object.

Therefore, according to the echo identification processor 10, theinfluence of the instantaneous change of the amplitude of the echosignal when identifying the target object may be reduced. Thus, thetarget object may accurately be identified.

Further, the echo identification processor 10 may identify the targetobject by using the characteristic vector CV generated from theplurality of characteristic amounts C1 to C4. Thus, characteristics ofthe target object may be grasped in multiple directions. As a result,the target object may be identified more accurately.

Further, the echo identification processor 10 may calculate thecharacteristic amount C1 based on the number N1 of samples constitutingthe rising portion of the partial sample sequence Rn, and identify thetarget object based on the characteristic amount C1. As a result, thetarget object may suitably be identified based on the rising portion.

Further, the echo identification processor 10 may calculate thecharacteristic amount C2 based on the highest value Vp among theamplitudes of the samples included in the partial sample sequence Rn,and identify the target object based on the characteristic amount C2. Asa result, the target object may suitably be identified based on thehighest value.

Further, the echo identification processor 10 may calculate thecharacteristic amount C3 based on the number N2 of samples constitutingthe falling portion of the partial sample sequence Rn, and identify thetarget object based on the characteristic amount C3. As a result, thetarget object may suitably be identified based on the falling portion.

Further, the echo identification processor 10 may calculate thecharacteristic amount C4 based on the value obtained on the basis of theintegral value A2 of the falling portion of the partial sample sequenceRn, and identify the target object based on the characteristic amountC4. As a result, the target object may suitably be identified based onthe integral value.

Further, the echo identification processor 10 may identify the targetobject based on the similarity between the characteristic vector graphG_(CV) generated based on the characteristic amounts C1 to C4 and thetemplates TP. As a result, one of the plurality of templates TP which isclosest to the characteristic vector graph G_(CV) may be selected.

Further, the echo identification processor 10 may calculate thesimilarity based on the difference between the characteristic amounts C1to C4 of the target object and the characteristic amounts C1 tmp to C4tmp of the templates TP. As a result, the similarity may suitably becalculated.

Modifications

Although the embodiment of the present disclosure is described above,the present disclosure is not limited to these embodiments, and variousmodifications may be made without departing from the spirit of thepresent disclosure.

(1) FIG. 9 is a block diagram of an echo identification processor 10 aof a radar apparatus according to a modification. Further, FIG. 10 is aflowchart illustrating an operation of the echo identification processor10 a illustrated in FIG. 9. Moreover, FIG. 11 is a graph illustratingone example of an echo sample sequence. The echo identificationprocessor 10 a of the radar apparatus according to this modification maybe different from the echo identification processor 10 of the aboveembodiment in configuration and operation. The echo identificationprocessor 10 a may include an extracting module 15, a characteristicvector generating module 16, a memory 17, and an identifying module 18.Hereinafter, the configuration and operation of the echo identificationprocessor 10 a may be described with reference to FIGS. 9 to 11.

The extracting module 15 may include a candidate object extractingsubmodule 15 a and an unnecessary object extracting submodule 15 b.

The candidate object extracting submodule 15 a may compare, withreference to FIG. 11, each of sample data constituting the echo samplesequence ES with a first threshold Thr1 stored therein (corresponding tothe threshold Thr of the above embodiment). If at least a given numberof successive sample data exceed the first threshold Thr1 in thedistance direction, the candidate object extracting submodule 15 a mayextract these sample data as a candidate object partial sample sequenceRAn (51 in FIG. 10). The extracted candidate object partial samplesequence RAn may be outputted to the characteristic vector generatingmodule 16. Note that, the candidate object partial sample sequence RAnmay be a partial sample sequence obtained from an object which may be atarget object candidate, and, as described later in detail, may becategorized into a partial sample sequence of the target object and apartial sample sequence of an unnecessary object (sea clutter etc.) asan object which is not a target object. Further, the value of the firstthreshold Thr1 described above may be changed by the operation of theuser.

The unnecessary object extracting submodule 15 b may compare, withreference to FIG. 11, each of sample data constituting the echo samplesequence with the first threshold Thr1 and a second threshold Thr2. Ifat least a given number of successive sample data fall below the firstthreshold Thr1 and also exceed the second threshold Thr2 in the distancedirection, the unnecessary object extracting submodule 15 b may extractthese sample data as an unnecessary object partial sample sequence (S7in FIG. 10). Note that, the unnecessary object partial sample sequencemay be a partial sample sequence caused by the unnecessary objectdescribed above. The extracted unnecessary object partial samplesequence may be outputted to the characteristic vector generating module16. Further, the object from which the partial sample sequence extractedas the unnecessary object partial sample sequence by the unnecessaryobject extracting submodule 15 b is generated, may be notified to theidentifying module 14 as the unnecessary object. Further, the value ofthe second threshold Thr2 described above may be changed by theoperation of the user.

Similarly to the case of the above embodiment, the characteristic vectorgenerating module 16 may generate the candidate object characteristicvector graph as a characteristic vector graph based on the candidateobject partial sample sequence RAn extracted by the candidate objectextracting submodule 15 a. Further, similarly to the case of the aboveembodiment, the characteristic vector generating module 16 may generatean unnecessary object characteristic vector graph as a characteristicvector graph based on the unnecessary object partial sample sequenceextracted by the unnecessary object extracting submodule 15 b (S8 inFIG. 10). The candidate object characteristic vector graph may beoutputted to the identifying module 18. On the other hand, theunnecessary object characteristic vector graph may be outputted to thememory 17.

FIG. 12 shows schematic views of templates stored in the memory 17illustrated in FIG. 9. Similarly to the case of the above embodiment,the memory 17 may store large ship templates, medium ship templates, andsmall ship templates for identifying the target object. These templatesmay be prepared in advance by experiments etc. similarly to the case ofthe above embodiment.

Further, the memory 17 may store unnecessary object templates(unnecessary object identification data) for identifying the unnecessaryobject. The memory 17 may store the unnecessary object characteristicvector graphs generated and outputted sequentially by the characteristicvector generating module 16 as the unnecessary object templates (S9 inFIG. 10). An upper limit number of the unnecessary object characteristicvector graphs stored in the memory 17 may be determined in advance, andwhen the number of the unnecessary object characteristic vector graphsgenerated from the characteristic vector generating module 16 exceedsthe upper limit number, the oldest unnecessary object template storedmay be deleted and the latest unnecessary object characteristic vectorgraph inputted may be stored as a new unnecessary object template. Notethat, the memory 17 may not store any unnecessary object template in aninitial state (state immediately after the apparatus is manufactured).

Based on the partial sample sequences extracted by the extracting module15 (the candidate object partial sample sequence and the unnecessaryobject partial sample sequence), the identifying module 18 may identifywhether the object from which the partial sample sequences are generatedis the target object or the unnecessary object. Further, when thepartial sample sequence is caused by the target object, the identifyingmodule 18 may identify which ship corresponds to the target object, alarge ship, a medium ship or a small ship.

For example, when the unnecessary object partial sample sequence isextracted by the extracting module 15, the object from which theunnecessary object partial sample sequence is generated may beidentified as the unnecessary object (that is, it is not identified asthe target object (S10 in FIG. 10). On the other hand, when theextracting module 15 extracts the candidate object partial samplesequence, the identifying module 18 may compare the characteristicvector graph generated from the candidate object partial sample sequencewith the templates (S2 in FIG. 10), and according to the comparisonresult, the identifying module 18 may identify the object from which thecharacteristic vector is generated, as one of the large ship, the mediumship, the small ship, and the unnecessary object (S3 to S6 in FIG. 10).

Meanwhile, the intensity of the echo signal of the object which isdesired to be detected by the radar apparatus may vary depending on thesurrounding environment etc. In this regard, the radar apparatus of thismodification may be configured such that the value of the firstthreshold Thr1 is adjustable by the user so that the object desired tobe detected may be detected. Thus, for example, even in an environmentin which the echo intensity of the object desired to be detected is low,the user may reduce the value of the first threshold Thr1 so that theobject may be detected. However in this case, an object, such as seaclutter which is not desired to be detected, may erroneously be detectedas the target object.

In this regard, in the echo identification processor 10 a of the radarapparatus of this modification, the characteristic vector graph obtainedfrom an unnecessary object, such as sea clutter, may be stored in thememory 17 as the unnecessary object template. Further, thecharacteristic vector graph of a candidate object which is a candidatefor the target object may be compared, not only with each ship template,but also with each unnecessary object template. Thus, when thecharacteristic vector graph of the object detected as the candidateobject has the highest similarity to the unnecessary object template,the object may not be detected as the target object. That is, by theuser reducing the value of the first threshold, the object which hasbeen erroneously detected once may be excluded from the target objectsby considering it as an unnecessary object. Therefore, according to theecho identification processor 10 a of this modification, the risk oferroneously detecting the unnecessary object as a target object may bereduced while the detection accuracy of the target object may beimproved.

As described above, according to the echo identification processor 10 aof the radar apparatus of this modification, the risk of erroneouslydetecting the unnecessary object as a target object may be reduced.

(2) FIG. 13 is a block diagram of an echo identification processor 10 bof a radar apparatus according to a modification. The echoidentification processor 10 b of the radar apparatus according to thismodification may include an extracting module 11, a characteristicvector generating module 12, a deterioration degree estimating module19, a memory 20, and an identifying module 21. Among these components,configurations and operations of the extracting module 11 and thecharacteristic vector generating module 12 may be the same as those ofthe extracting module 11 and the characteristic vector generating module12 of the echo identification processor 10 of the above embodiment andthe detailed description thereof is omitted.

The deterioration degree estimating module 19 may estimate adeterioration degree of the partial sample sequence extracted by theextracting module 11. Echoes caused by other objects (rain, sea clutter,etc.) may be superimposed on an echo caused by a target object, whichmay cause distortion of the echo of the target object desired to beobserved. The deterioration degree may be an index indicating the degreeof difference of a partial sample sequence actually extracted, from thepartial sample sequence corresponding to only the target object. Forexample, the partial sample sequence having a low deterioration degreemay be a partial sample sequence extracted in a situation where thenumber of the other objects (rain, see clutter, etc.) described above issmall. On the other hand, the partial sample sequence having a highdeterioration degree may be a partial sample sequence extracted in asituation where the number of the other objects is large.

For example, the deterioration degree estimating module 19 may calculatea difference between a highest value of the partial sample sequence anda threshold Thr as a deterioration degree reference value, and calculatethe deterioration degree according to the deterioration degree referencevalue. The deterioration degree may be classified into, for example,three levels of low, medium and high. The deterioration degreeestimating module 19 may estimate that the deterioration degree is lowwhen the deterioration degree reference value is a relatively low value,that the deterioration degree is medium when the deterioration degreereference value is a medium value, and that the deterioration degree ishigh when the deterioration degree reference value is a relatively highvalue. The deterioration degree estimated by the deterioration degreeestimating module 19 may be notified to the identifying module 21.

FIG. 14 shows views illustrating templates stored in the memory 20illustrated in FIG. 13. The templates stored in the memory 20 of thismodification may also be prepared in advance by experiments etc.,similarly to the templates stored in the memory 13 of the aboveembodiment. The templates of this modification may be classified andstored for each deterioration degree described above. For example, withreference to FIG. 14, the plurality of templates stored in the memory 20may be classified into one of a template group (high-deteriorationdegree template group) classified into a group of high deteriorationdegree, a template group (medium-deterioration degree template group)classified into a group of medium deterioration degree, and a templategroup (low-deterioration degree template group) classified into a groupof low deterioration degree. Each group of templates may includetemplates of a large ship, templates of a medium ship, and templates ofa small ship.

The identifying module 21 may include a template group selectingsubmodule 21 a (data group selecting module). The template groupselecting submodule 21 a may select one of the three template groupsbased on the deterioration degree notified from the deterioration degreeestimating module 19. For example, the template group selectingsubmodule 21 a may select the low deterioration degree template groupwhen the deterioration degree notified from the deterioration degreeestimating module 19 is low, the medium deterioration degree templategroup when the deterioration degree notified from the deteriorationdegree estimating module 19 is medium, and the high deterioration degreetemplate group when the deterioration degree notified from thedeterioration degree estimating module 19 is high. Then, the identifyingmodule 21 may compare each of the templates constituting the selectedtemplate group with the characteristic vector graph G_(CV) to calculatethe similarity, and the identifying module 21 may notify a display imagegenerating module 5 of the size of the ship corresponding to thetemplate with the highest similarity (the large ship, the medium ship,or the small ship), similarly to the case of the above embodiment.

As described above, in the echo identification processor 10 b of theradar apparatus of this modification, the partial sample sequence of thetarget object may be compared with the templates included in a templategroup prepared for each deterioration degree of the partial samplesequence (type-based data group for each deterioration degree). Thus,even when noises, such as rain or sea clutter etc., are superimposed onthe echo of the target object, since the target object may be identifiedusing the template group in which the influence of these noises aretaken into consideration in advance, the object may be identified moresuitably.

(3) FIG. 15 is a block diagram of an echo identification processor 10 cof a radar apparatus according to a modification. Further, FIG. 16 is aview illustrating one example of an image displayed on a display unit 6illustrated in FIG. 15. The echo identification processor 10 c of theradar apparatus of this modification may include an extracting module11, a characteristic vector generating module 12, a memory 13, and anidentifying module 22. Among these components, the extracting module 11,the characteristic vector generating module 12, and the memory 13 may bethe same as those in the above embodiment. Thus, the description thereofis omitted.

As in the case of the above embodiment, the identifying module 22 may beconfigured to identify the small ship, the medium ship, and the largeship. Among these ships, the identification processes of the medium andlarge ships may be the same as in the above embodiment. However, theidentification process of the small ship may be different from the aboveembodiment.

The identifying module 22 may include a small ship identifying submodule22 a. The identifying module 22 may extract partial sample sequenceswith highest similarity to the template of the small ship, as small shipcandidate partial sample sequences. For the respective small shipcandidate partial sample sequences thus extracted, the small shipidentifying submodule 22 a may select small ship candidate partialsample sequences at closest positions to each other, and when the mutualdistance therebetween is relatively long (above a given value), thesmall ship candidate partial sample sequences may not be identified asthe small ship. On the other hand, when the mutual distance is short(below the given value), the small ship candidate partial samplesequences may be identified as the small ship.

For example, to describe with reference to FIG. 16, the small shipcandidate partial sample sequence located at Pt1 may be relativelydistant from positions of other small ship candidate partial samplesequences (Pt2 to Pt6). Therefore, the small ship candidate partialsample sequence may not be identified as the small ship, and the smallship marker MKn may not be displayed on the echo image. Similarly, thesmall ship candidate partial sample sequences of which positions areindicated by Pt2 to Pt6 may also not be identified as the small ship.

On the other hand, to also describe with reference to FIG. 16, the smallship candidate partial sample sequence located at Pt7 may be close topositions (Pt8 and Pt9) of other small ship candidate partial samplesequences. Therefore, the small ship candidate partial sample sequencesmay be identified as the small ship, and a marker MK2 for the small shipmay be displayed on the echo image.

Generally, even with a small object, partial sample sequences caused bythe small object may be obtained successively over a plurality of pointsin the circumferential direction with reference to the ship position S.In other words, the partial sample sequences not obtained successivelyin the circumferential direction may highly possibly be caused by noise,such as sea clutter, and not by the target object. In this regard, asdescribed above, the small ship candidate partial sample sequences ofwhich mutual distance is short in the circumferential direction may beidentified as the small ship, and the small ship candidate partialsample sequence far from the other small ship candidate partial samplesequences may not be identified as the small ship. Thus, the possibilityof erroneously identifying the unnecessary echo as the small object maybe reduced.

As described above, according to the echo identification processor 10 cof the radar apparatus of this modification, the possibility oferroneously identifying the unnecessary echo as the small object may bereduced.

(4) FIG. 17 is a block diagram of an echo identification processor 10 dof a radar apparatus according to a modification. The echoidentification processor 10 d of the radar apparatus of thismodification may include an extracting module 11, a characteristicvector generating module 12, a memory 13, and an identifying module 23.Among these components, the extracting module 11, the characteristicvector generating module 12, and the memory 13 may be the same as thosein the above embodiment. Thus, the description thereof is omitted.

Similarly to the case of the above embodiment, the identifying module 23may be configured to identify a small ship, a medium ship, and a largeship. Among these ships, the identification process of the large shipmay be the same as in the above embodiment. However, the identificationprocesses of the medium object and the small ship may be different fromthe above embodiment.

The identifying module 23 may include a medium ship identifyingsubmodule 23 a and a small ship identifying submodule 23 b. Theidentifying module 23 may extract partial sample sequences with highestsimilarity to a template of the medium ship, as medium ship candidatepartial sample sequences. For the respective medium ship candidatepartial sample sequences thus extracted, when the large ship is locatedon the ship side of the medium ship candidate partial sample sequences,the medium ship identifying submodule 23 a may not identify the mediumship candidate partial sample sequences as the target object. Note that,each of the medium ship candidate partial sample sequencesillustratively described here may correspond to a far-side partialsample sequence. Further, each of the partial sample sequences generatedfrom the large ship, which are illustratively described here, maycorrespond to a close-side partial sample sequence.

Similarly, the identifying module 23 may extract partial samplesequences with highest similarity to a template of the small ship, assmall ship candidate partial sample sequences. For the respective smallship candidate partial sample sequences thus extracted, when a large ormedium ship is located on the ship side of the small ship candidatepartial sample sequences, the small ship identifying submodule 23 b maynot identify the small ship candidate partial sample sequences as thetarget object. Note that, each of the small ship candidate partialsample sequences illustratively described here may correspond to thefar-side partial sample sequence. Further, each the partial samplesequences generated from the large and medium ships, which areillustratively described here, may correspond to the close-side partialsample sequence.

FIG. 18 is a view illustrating one example of an image displayed on adisplay unit 6 illustrated in FIG. 17. With reference to FIG. 18, on theship position S side of an echo image TG2 generated from the small shipcandidate partial sample sequences, an echo image TG1 larger than theecho image TG2 may be located in the same azimuth direction (straightline L direction) with reference to the ship position S. Therefore,markers MK1, MK3 to MK5 as assigned to other echo images TG1, TG3 to TG5may not be assigned to the echo image TG2.

Meanwhile, as illustrated in FIG. 18, when a relatively large object(corresponding to TG1) is detected close to the ship and an object(corresponding to TG2) smaller than the large object may be detected onthe far side at the same azimuth, the small object may highly possiblybe an echo caused by multiple reflections of the transmission wave. Thisis because, when a large object is located close to the ship, it may beconsidered that the transmission wave does not reach the object locatedfarther than the large object at the same azimuth. In this regard, whenthe size of the object on the far side from the ship at the same azimuthis smaller than the size of the object on the close side to the ship,the small object may not be detected as the target object as in the echoidentification processor 10 d of this modification. Thus, it may beprevented that a false image caused by multiple reflections iserroneously identified as the target object.

As described above, the echo identification processor 10 d of the radarapparatus of this modification may prevent erroneous identification ofthe false image caused by multiple reflections as the target object.

Note that, with reference to FIG. 19, in the radar apparatus of thismodification, in order to facilitate the understanding that the echoimage TG2 estimated to be the false image is caused by multiplereflections, for example, the echo image TG2 may be surrounded by amarker MK2 indicated by a dashed line as illustrated in FIG. 19. Thus,the user may estimate that the echo image TG2 is the false image.

(5) FIG. 20 is a block diagram of a radar apparatus 1 a according to amodification. Further, FIG. 21 is a block diagram of an echoidentification processor 10 e illustrated in FIG. 20. As illustrated inFIG. 20, the radar apparatus 1 a of this modification may include apositioning unit 25 and a buoy information acquirer 26 in addition tothe respective components provided to the radar apparatus 1 a of theabove embodiment. Further, the echo identification processor 10 e mayinclude a buoy identifying submodule 27 a in addition to the respectivecomponents provided to the echo identification processor 10 of the aboveembodiment. The buoy identifying submodule 27 a may be included in anidentifying module 27.

The positioning unit 25 may be configured by a positioning device, suchas a GPS receiver. The positioning unit 25 may acquire a ship positionand heading at a time point when the radar apparatus of thismodification acquires a sweep signal.

The buoy information acquirer 26 may store chart information (nauticalchart information) including positions of buoys in a marine area wherethe ship travels. The buoy information acquirer 26 may acquirepositional information of buoys within a given distance range from theship, among the buoys included in the chart information.

The buoy identifying submodule 27 a may identify buoys from the targetobjects identified as small ships by the identifying module 27. Forexample, the buoy identifying submodule 27 a may compare the position ofeach target object identified as the small ship by the identifyingmodule 27 with the positional information of each buoy acquired by thebuoy information acquirer 26, to calculate a distance therebetween.Further, when the distance is short (within a given range), the buoyidentifying submodule 27 a may identify that the target objectidentified as the small ship is the buoy. On a display unit 6, an echoimage of the object identified as the buoy may be assigned with, forexample, a symbol indicating that the echo image is caused by the buoy.

As described above, the echo identification processor 10 e of the radarapparatus of this modification may identify whether the target object isthe buoy.

(6) In the above embodiment, the characteristic vector graph G_(CV) maybe created and the characteristic vector graph G_(CV) and the templateTP may be compared with each other; however, without limiting to this,the characteristic vector CV which is the basis of the characteristicvector graph G_(CV) may be compared with the template TP.

(7) In the above embodiment, the characteristic vector CV may be createdfrom the plurality of characteristic amounts C1 to C4, and thecharacteristic vector graph G_(CV) obtained from the characteristicvector CV and the template TP may be compared with each other; however,it may not be limited to this. For example, the characteristic amountscalculated from the partial sample sequence may be compared with thecharacteristic amounts constituting the template.

(8) In the above embodiment, the above characteristic amounts C1 to C4may be calculated as the characteristic amounts; however, othercharacteristic amounts may be used. For example, the characteristicamounts may be a ratio between an average value and a peak value in therising portion of the echo sample sequence, or a ratio between anaverage value and a peak value in the falling portion of the echo samplesequence, etc.

DESCRIPTION OF REFERENCE CHARACTERS

-   1, 1 a Radar Apparatus-   2 a Antenna (Transducer)-   10, 10 a-10 e Echo Identification Processor (Signal Processor)-   11, 15 Extracting Module-   12, 16 Characteristic Vector Generating Module (Characteristic    Amount Calculating Module)

1. A signal processor for detecting a target object by using atransducer configured to transmit a transmission wave and receiving areflection wave of the transmission wave, based on a reception signalobtained from the reflection wave, the signal processor comprising:processing circuitry configured to extract, from echo sample sequenceseach of which is obtained by plotting a plurality of samplesconstituting the reception signal on coordinates defined by a distancefrom the transducer and amplitudes of the plurality of samples and isgenerated for each azimuth with reference to a position of thetransducer, a plurality of samples caused by the target object as apartial sample sequence; and to calculate a characteristic of thepartial sample sequence as a characteristic amount; and a memoryconfigured to store a plurality of type-based data that are data ascomparison targets of the characteristic amount and correspond to typesfrom which the target object is identified; the processing circuitrybeing further configured to compare the characteristic amount with eachof the plurality of type-based data; and to identify the type of thetarget object for which the characteristic amount is calculated based onthe comparison result.
 2. The signal processor of claim 1, wherein theprocessing circuitry calculates the plurality of characteristic amountsand generates a characteristic vector from the plurality ofcharacteristic amounts.
 3. The signal processor of claim 1, wherein thecharacteristic amount is one of the number of samples constituting arising portion of the partial sample sequence, the number of samplesconstituting a falling portion of the partial sample sequence, a highestvalue of the amplitudes of the samples included in the partial samplesequence, and a value obtained based on an integral value of the fallingportion.
 4. The signal processor of claim 1, wherein the processingcircuitry identifies the target object based on similarity between thecharacteristic amount and the type-based data.
 5. The signal processorof claim 4, wherein the similarity is calculated based on a differencebetween the characteristic amount and the type-based data.
 6. The signalprocessor of claim 1, wherein the type corresponds to the size of thetarget object.
 7. The signal processor of claim 1, wherein, the memoryfurther stores unnecessary object identification data as data that is acomparison target of the characteristic amount; and the processingcircuitry compares the characteristic amount with the unnecessary objectidentification data and identifies, based on the comparison result, thatthe partial sample sequence for which the characteristic amount iscalculated is not caused by the target object.
 8. The signal processorof claim 1, wherein the processing circuitry is further configured toestimate a deterioration degree that is a degree of deterioration of thepartial sample sequence, the memory storing the plurality of type-baseddata classified for each deterioration degree, as type-based data groupsfor each deterioration degree; and to select, based on the deteriorationdegree, a type-based data group for each deterioration degree to becompared with the partial sample sequence of which the deteriorationdegree is estimated, wherein the processing circuitry compares thecharacteristic amount with each of the plurality of type-based dataconstituting the type-based data group for each deterioration degree. 9.The signal processor of claim 1, wherein, the plurality of types fromwhich the target object is identified include at least a large objectand a small object indicating the size of the target object, and when aposition of a certain one of the partial sample sequences that isidentified to be the small object and a position of another one of thepartial sample sequences that is identified to be the small object haveat least a given distance from each other, the processing circuitryidentifies that the certain partial sample sequence is not caused by thetarget object.
 10. The signal processor of claim 1, wherein, theplurality of types from which the target object is identified aredetermined in advance according to the size of the target object, andwhen a close-side partial sample sequence and a far-side partial samplesequence that are at least two partial sample sequences are extractedfrom the echo sample sequences, and the size of the target objectindicated by the type of the far-side partial sample sequence that has alonger distance than the close-side partial sample sequence from thetransducer is smaller than the size of the target object indicated bythe type of the close-side partial sample sequence, the processingcircuitry identifies that the far-side partial sample sequence is notcaused by the target object.
 11. An echo identification apparatus,comprising: a transducer configured to transmit a transmission wave andreceive a reflection wave of the transmission wave; processing circuitryconfigured to detect a target object by using the transducer, based on areception signal obtained from the reflection wave; to extract, fromecho sample sequences each of which is obtained by plotting a pluralityof samples constituting the reception signal on coordinates defined by adistance from the transducer and amplitudes of the plurality of samplesand is generated for each azimuth with reference to a position of thetransducer, a plurality of samples caused by the target object as apartial sample sequence; and to calculate a characteristic of thepartial sample sequence as a characteristic amount; and a memoryconfigured to store a plurality of type-based data that are data ascomparison targets of the characteristic amount and correspond to typesfrom which the target object is identified; the processing circuitrybeing further configured to compare the characteristic amount with eachof the plurality of type-based data; and to identify the type of thetarget object for which the characteristic amount is calculated based onthe comparison result.
 12. (canceled)
 13. The echo identificationapparatus of claim 11, wherein the processing circuitry calculates theplurality of characteristic amounts and generates a characteristicvector from the plurality of characteristic amounts.
 14. The echoidentification apparatus of claim 11, wherein the characteristic amountis one of the number of samples constituting a rising portion of thepartial sample sequence, the number of samples constituting a fallingportion of the partial sample sequence, a highest value of theamplitudes of the samples included in the partial sample sequence, and avalue obtained based on an integral value of the falling portion. 15.The echo identification apparatus of claim 11, wherein the processingcircuitry identifies the target object based on similarity between thecharacteristic amount and the type-based data.
 16. The echoidentification apparatus of claim 15, wherein the similarity iscalculated based on a difference between the characteristic amount andthe type-based data.
 17. The echo identification apparatus of claim 11,wherein, the memory further stores unnecessary object identificationdata as data that is a comparison target of the characteristic amount;and the processing circuitry compares the characteristic amount with theunnecessary object identification data and identifies, based on thecomparison result, that the partial sample sequence for which thecharacteristic amount is calculated is not caused by the target object.18. The echo identification apparatus of claim 11, wherein theprocessing circuitry is further configured to estimate a deteriorationdegree that is a degree of deterioration of the partial sample sequence,the memory storing the plurality of type-based data classified for eachdeterioration degree, as type-based data groups for each deteriorationdegree; and to select, based on the deterioration degree, a type-baseddata group for each deterioration degree to be compared with the partialsample sequence of which the deterioration degree is estimated, whereinthe processing circuitry compares the characteristic amount with each ofthe plurality of type-based data constituting the type-based data groupfor each deterioration degree.
 19. The echo identification apparatus ofclaim 11, wherein, the plurality of types from which the target objectis identified include at least a large object and a small objectindicating the size of the target object; and when a position of acertain one of the partial sample sequences that is identified to be thesmall object and a position of another one of the partial samplesequences that is identified to be the small object have at least agiven distance from each other, the processing circuitry identifies thatthe certain partial sample sequence is not caused by the target object.20. The signal echo identification apparatus of claim 11, wherein, theplurality of types from which the target object is identified aredetermined in advance according to the size of the target object, andwhen a close-side partial sample sequence and a far-side partial samplesequence that are at least two partial sample sequences are extractedfrom the echo sample sequences, and the size of the target objectindicated by the type of the far-side partial sample sequence that has alonger distance than the close-side partial sample sequence from thetransducer is smaller than the size of the target object indicated bythe type of the close-side partial sample sequence, the processingcircuitry identifies that the far-side partial sample sequence is notcaused by the target object.
 21. The echo identification apparatus ofclaim 11, equipped on a ship, wherein the processing circuitry isfurther configured to perform positioning of the ship; and to acquirenautical chart information including positions of buoys; and positionalinformation of a buoy within a given distance range from the ship amongthe buoys in the nautical chart information, wherein the processingcircuitry identifies that the target object is the buoy based on thepositional information of the buoy and positional information of thetarget object for which the partial sample sequence is generated.